Architect Data Engineer

Role Overview

This role sits at the intersection of data architecture, AI platform engineering, and enterprise-scale system design, with a strong focus on enabling next-generation Agentic AI capabilities. You will define and lead the architectural vision for a modern, high-performance data layer that unifies structured, unstructured, and graph-based data across complex hybrid environments.

What You Will Do

Define and lead the end-to-end architecture for a modern data platform supporting Agentic AI, integrating structured, unstructured, and graph-based data systems. Design multi-tenant schemas and knowledge graph ontologies to enable advanced reasoning, contextual understanding, and cross-domain data retrieval for AI agents.

Why It Might Be a Fit

This is a high-impact role combining deep technical leadership with client-facing strategic influence. You will collaborate directly with clients as a technical authority, translating business needs into robust, production-grade architectures.

Requirements

• 10+ years of experience in data engineering, data architecture, or platform engineering roles within enterprise or AI-driven environments.
• Strong expertise in architecting hybrid data ecosystems using platforms such as Snowflake, Kinetica, NoSQL, and graph databases.
• Deep knowledge of knowledge graph design, including RDF or property graph modeling for enterprise-scale systems.
• Proven experience designing and optimizing ETL/ELT pipelines, including streaming, batch, and CDC architectures.
• Strong understanding of database internals, including indexing strategies, partitioning, scaling, and performance tuning in cloud environments.
• Experience building data platforms that support real-time analytics, AI/ML, or agent-based systems.
• Client-facing experience as a technical lead, including running workshops, gathering requirements, and defining architectural roadmaps.
• Familiarity with semantic layers (e.g., dbt Semantic Layer, Cube) is a strong plus.
• Knowledge of data security principles such as RBAC and row-level security in distributed systems.
• Strong communication and stakeholder management skills with a consultative mindset.

Benefits

• Competitive compensation aligned with experience and market standards.
• Remote work flexibility within Canada or the US East region.
• Opportunity to work on cutting-edge Agentic AI and enterprise data platform architectures.
• Exposure to Fortune 500 clients and large-scale AI transformation initiatives.
• Access to advanced cloud, AI, and data technologies in a research-driven environment.
• Strong culture of innovation, learning, and continuous technical upskilling.
• Collaborative environment working alongside highly skilled AI and data engineering teams.
• Opportunity to influence next-generation data architectures powering real-world AI systems.